Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations, the description discloses concrete behavioral traits: refusal reasons, the exact success/refusal response shapes, the grounding constraint to only the tool result, and the extra LLM call cost. This goes well beyond what readOnlyHint, openWorldHint, and idempotentHint already communicate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured and front-loaded with the most important distinction. Every sentence contributes either purpose, usage boundaries, response contract, or cost trade-off; nothing feels redundant or wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the annotations and schema, the description covers the tool's behavior, when to use it, what it returns on success, what refusal reasons look like, and how it compares to the sibling tool. It is complete enough for an agent to select and invoke it correctly without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the question parameter is already documented with its accepted aliases. The description does not add parameter-specific semantics, but the schema fully carries that burden, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a 'hallucination-resistant answer mode' and contrasts it with ask_pipeworx, making its distinct role obvious. It states a specific behavior: extract answers using only the tool result content, and explicitly names the sibling it mirrors while differentiating its grounded output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative, ask_pipeworx, and tells the agent to prefer that for casual lookups, including the cost trade-off of one extra LLM call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.